Overview

Dataset statistics

Number of variables38
Number of observations3240
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory822.8 KiB
Average record size in memory260.0 B

Variable types

Numeric24
Categorical9
DateTime5

Warnings

msno has a high cardinality: 3240 distinct values High cardinality
bd is highly correlated with genderHigh correlation
gender is highly correlated with bdHigh correlation
total_payment_channels is highly correlated with total_transactions and 3 other fieldsHigh correlation
change_in_payment_methods is highly correlated with change_in_planHigh correlation
payment_plan_days_mean is highly correlated with plan_list_price_mean and 1 other fieldsHigh correlation
change_in_plan is highly correlated with change_in_payment_methodsHigh correlation
plan_list_price_mean is highly correlated with payment_plan_days_mean and 2 other fieldsHigh correlation
actual_amount_paid_mean is highly correlated with payment_plan_days_mean and 2 other fieldsHigh correlation
is_auto_renew_mean is highly correlated with is_autorenew_change_flagHigh correlation
is_autorenew_change_flag is highly correlated with is_auto_renew_meanHigh correlation
total_transactions is highly correlated with total_payment_channels and 3 other fieldsHigh correlation
is_cancel_mean is highly correlated with is_cancel_change_flagHigh correlation
is_cancel_change_flag is highly correlated with is_cancel_meanHigh correlation
discount_mean is highly correlated with is_discount_mean and 1 other fieldsHigh correlation
is_discount_mean is highly correlated with discount_mean and 1 other fieldsHigh correlation
is_discount_max is highly correlated with discount_mean and 1 other fieldsHigh correlation
membership_duration_mean is highly correlated with plan_list_price_mean and 1 other fieldsHigh correlation
more_than_30_sum is highly correlated with total_payment_channels and 2 other fieldsHigh correlation
num_25 is highly correlated with num_50 and 2 other fieldsHigh correlation
num_50 is highly correlated with num_25 and 2 other fieldsHigh correlation
num_75 is highly correlated with num_25 and 2 other fieldsHigh correlation
num_985 is highly correlated with num_25 and 2 other fieldsHigh correlation
num_100 is highly correlated with num_unq and 1 other fieldsHigh correlation
num_unq is highly correlated with num_100 and 1 other fieldsHigh correlation
total_secs is highly correlated with num_100 and 1 other fieldsHigh correlation
login_freq is highly correlated with total_payment_channels and 2 other fieldsHigh correlation
registration_duration is highly correlated with total_payment_channels and 3 other fieldsHigh correlation
bd is highly correlated with genderHigh correlation
gender is highly correlated with bdHigh correlation
total_payment_channels is highly correlated with total_transactions and 3 other fieldsHigh correlation
change_in_payment_methods is highly correlated with change_in_plan and 1 other fieldsHigh correlation
change_in_plan is highly correlated with change_in_payment_methodsHigh correlation
plan_list_price_mean is highly correlated with actual_amount_paid_mean and 2 other fieldsHigh correlation
actual_amount_paid_mean is highly correlated with plan_list_price_mean and 2 other fieldsHigh correlation
is_auto_renew_mean is highly correlated with change_in_payment_methods and 3 other fieldsHigh correlation
is_autorenew_change_flag is highly correlated with is_auto_renew_meanHigh correlation
total_transactions is highly correlated with total_payment_channels and 3 other fieldsHigh correlation
is_cancel_mean is highly correlated with is_cancel_change_flagHigh correlation
is_cancel_change_flag is highly correlated with is_cancel_meanHigh correlation
discount_mean is highly correlated with is_discount_mean and 1 other fieldsHigh correlation
is_discount_mean is highly correlated with discount_mean and 1 other fieldsHigh correlation
is_discount_max is highly correlated with discount_mean and 1 other fieldsHigh correlation
amt_per_day_mean is highly correlated with plan_list_price_mean and 1 other fieldsHigh correlation
more_than_30_sum is highly correlated with total_payment_channels and 2 other fieldsHigh correlation
num_25 is highly correlated with num_50 and 2 other fieldsHigh correlation
num_50 is highly correlated with num_25 and 2 other fieldsHigh correlation
num_75 is highly correlated with num_25 and 2 other fieldsHigh correlation
num_985 is highly correlated with num_25 and 2 other fieldsHigh correlation
num_100 is highly correlated with num_unq and 1 other fieldsHigh correlation
num_unq is highly correlated with num_100 and 1 other fieldsHigh correlation
total_secs is highly correlated with num_100 and 1 other fieldsHigh correlation
login_freq is highly correlated with total_payment_channels and 2 other fieldsHigh correlation
registration_duration is highly correlated with total_payment_channels and 3 other fieldsHigh correlation
bd is highly correlated with genderHigh correlation
gender is highly correlated with bdHigh correlation
total_payment_channels is highly correlated with total_transactions and 2 other fieldsHigh correlation
change_in_payment_methods is highly correlated with change_in_plan and 1 other fieldsHigh correlation
change_in_plan is highly correlated with change_in_payment_methodsHigh correlation
plan_list_price_mean is highly correlated with actual_amount_paid_mean and 2 other fieldsHigh correlation
actual_amount_paid_mean is highly correlated with plan_list_price_mean and 2 other fieldsHigh correlation
is_auto_renew_mean is highly correlated with change_in_payment_methods and 3 other fieldsHigh correlation
is_autorenew_change_flag is highly correlated with is_auto_renew_meanHigh correlation
total_transactions is highly correlated with total_payment_channels and 2 other fieldsHigh correlation
is_cancel_mean is highly correlated with is_cancel_change_flagHigh correlation
is_cancel_change_flag is highly correlated with is_cancel_meanHigh correlation
discount_mean is highly correlated with is_discount_mean and 1 other fieldsHigh correlation
is_discount_mean is highly correlated with discount_mean and 1 other fieldsHigh correlation
is_discount_max is highly correlated with discount_mean and 1 other fieldsHigh correlation
amt_per_day_mean is highly correlated with plan_list_price_mean and 1 other fieldsHigh correlation
more_than_30_sum is highly correlated with total_payment_channels and 1 other fieldsHigh correlation
num_25 is highly correlated with num_50High correlation
num_50 is highly correlated with num_25 and 1 other fieldsHigh correlation
num_75 is highly correlated with num_50 and 1 other fieldsHigh correlation
num_985 is highly correlated with num_75High correlation
num_100 is highly correlated with num_unq and 1 other fieldsHigh correlation
num_unq is highly correlated with num_100 and 1 other fieldsHigh correlation
total_secs is highly correlated with num_100 and 1 other fieldsHigh correlation
registration_duration is highly correlated with total_payment_channels and 1 other fieldsHigh correlation
change_in_plan is highly correlated with total_transactions and 4 other fieldsHigh correlation
amt_per_day_mean is highly correlated with total_transactions and 11 other fieldsHigh correlation
city is highly correlated with bd and 2 other fieldsHigh correlation
total_transactions is highly correlated with change_in_plan and 5 other fieldsHigh correlation
is_autorenew_change_flag is highly correlated with is_churn and 3 other fieldsHigh correlation
is_churn is highly correlated with is_autorenew_change_flag and 1 other fieldsHigh correlation
bd is highly correlated with city and 2 other fieldsHigh correlation
is_auto_renew_mean is highly correlated with change_in_plan and 3 other fieldsHigh correlation
payment_plan_days_mean is highly correlated with amt_per_day_mean and 4 other fieldsHigh correlation
change_in_payment_methods is highly correlated with is_auto_renew_meanHigh correlation
total_payment_channels is highly correlated with change_in_plan and 5 other fieldsHigh correlation
num_985 is highly correlated with num_50 and 2 other fieldsHigh correlation
is_cancel_change_flag is highly correlated with is_cancel_mean and 1 other fieldsHigh correlation
is_cancel_mean is highly correlated with amt_per_day_mean and 3 other fieldsHigh correlation
num_50 is highly correlated with num_985 and 2 other fieldsHigh correlation
num_75 is highly correlated with num_985 and 3 other fieldsHigh correlation
is_discount_max is highly correlated with amt_per_day_mean and 3 other fieldsHigh correlation
membership_expire_date_max is highly correlated with amt_per_day_mean and 5 other fieldsHigh correlation
login_freq is highly correlated with total_transactions and 2 other fieldsHigh correlation
gender is highly correlated with amt_per_day_mean and 2 other fieldsHigh correlation
plan_list_price_mean is highly correlated with amt_per_day_mean and 4 other fieldsHigh correlation
registration_duration is highly correlated with change_in_plan and 5 other fieldsHigh correlation
is_discount_mean is highly correlated with amt_per_day_mean and 3 other fieldsHigh correlation
num_25 is highly correlated with num_985 and 3 other fieldsHigh correlation
total_secs is highly correlated with num_unq and 1 other fieldsHigh correlation
more_than_30_sum is highly correlated with total_transactions and 4 other fieldsHigh correlation
actual_amount_paid_mean is highly correlated with amt_per_day_mean and 4 other fieldsHigh correlation
num_unq is highly correlated with num_75 and 3 other fieldsHigh correlation
membership_duration_mean is highly correlated with payment_plan_days_mean and 2 other fieldsHigh correlation
discount_mean is highly correlated with amt_per_day_mean and 3 other fieldsHigh correlation
registered_via is highly correlated with change_in_plan and 6 other fieldsHigh correlation
num_100 is highly correlated with total_secs and 1 other fieldsHigh correlation
is_autorenew_change_flag is highly correlated with registered_viaHigh correlation
registered_via is highly correlated with is_autorenew_change_flagHigh correlation
membership_duration_mean is highly skewed (γ1 = -31.16723676) Skewed
msno is uniformly distributed Uniform
df_index has unique values Unique
msno has unique values Unique
is_auto_renew_mean has 487 (15.0%) zeros Zeros
is_cancel_mean has 2770 (85.5%) zeros Zeros
discount_mean has 2911 (89.8%) zeros Zeros
is_discount_mean has 3026 (93.4%) zeros Zeros
more_than_30_sum has 428 (13.2%) zeros Zeros
num_25 has 134 (4.1%) zeros Zeros
num_50 has 556 (17.2%) zeros Zeros
num_75 has 844 (26.0%) zeros Zeros
num_985 has 815 (25.2%) zeros Zeros
num_100 has 41 (1.3%) zeros Zeros

Reproduction

Analysis started2023-05-19 10:59:18.921773
Analysis finished2023-05-19 11:00:38.432253
Duration1 minute and 19.51 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct3240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161888.8309
Minimum14
Maximum323987
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2023-05-19T11:00:38.526085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile15865.25
Q180864.5
median161963
Q3244588
95-th percentile307726.4
Maximum323987
Range323973
Interquartile range (IQR)163723.5

Descriptive statistics

Standard deviation94433.52726
Coefficient of variation (CV)0.5833233013
Kurtosis-1.224760133
Mean161888.8309
Median Absolute Deviation (MAD)81636
Skewness0.006914496358
Sum524519812
Variance8917691071
MonotonicityNot monotonic
2023-05-19T11:00:38.675526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2527371
 
< 0.1%
1603381
 
< 0.1%
1439451
 
< 0.1%
2833951
 
< 0.1%
982621
 
< 0.1%
1178281
 
< 0.1%
2885321
 
< 0.1%
172701
 
< 0.1%
539871
 
< 0.1%
675221
 
< 0.1%
Other values (3230)3230
99.7%
ValueCountFrequency (%)
141
< 0.1%
701
< 0.1%
1231
< 0.1%
1261
< 0.1%
1831
< 0.1%
3001
< 0.1%
3161
< 0.1%
3611
< 0.1%
3931
< 0.1%
4541
< 0.1%
ValueCountFrequency (%)
3239871
< 0.1%
3237981
< 0.1%
3237431
< 0.1%
3237171
< 0.1%
3236651
< 0.1%
3234611
< 0.1%
3233881
< 0.1%
3233701
< 0.1%
3233481
< 0.1%
3233291
< 0.1%

msno
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct3240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
T4TLe/jOIP0/Bg9kOKR3rd19XVZDIHlVzvS3DhpweIk=
 
1
oYyjgI3EMtpJamxLBTROcYLXR3spcB/4zXrqtTQFM74=
 
1
6y0f6LY1AXh1qAI68f0faAKjgU/DPp+j63xTnsmdtT4=
 
1
hsR0Pfz3iJtzva17DoxOqq20i4WfYFKLN/wgT914vrI=
 
1
ZQbQiWQgoyPDP/cYkgDC0tCzFIjjBKIEEiU3/YGApMo=
 
1
Other values (3235)
3235 

Length

Max length44
Median length44
Mean length44
Min length44

Characters and Unicode

Total characters142560
Distinct characters65
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3240 ?
Unique (%)100.0%

Sample

1st rowT4TLe/jOIP0/Bg9kOKR3rd19XVZDIHlVzvS3DhpweIk=
2nd row3twUh3ugQEnQyGAyOxT4uPhpMht2EPW1vl6/k7J+RdI=
3rd row/1X44AKG3S5AoBAJeokIkqLvgRaz+h5ElPncLqWnWys=
4th row1Ufg2Ep/dzom8G0hN+0UWbmxpRPFrGt4D4Vh2yd4pOA=
5th rowZozytlgZZhHBVCTHGug8CxREhiV9QpS+TJ5e+z06gVA=

Common Values

ValueCountFrequency (%)
T4TLe/jOIP0/Bg9kOKR3rd19XVZDIHlVzvS3DhpweIk=1
 
< 0.1%
oYyjgI3EMtpJamxLBTROcYLXR3spcB/4zXrqtTQFM74=1
 
< 0.1%
6y0f6LY1AXh1qAI68f0faAKjgU/DPp+j63xTnsmdtT4=1
 
< 0.1%
hsR0Pfz3iJtzva17DoxOqq20i4WfYFKLN/wgT914vrI=1
 
< 0.1%
ZQbQiWQgoyPDP/cYkgDC0tCzFIjjBKIEEiU3/YGApMo=1
 
< 0.1%
E/+UHeBM49ngCPpd/Vaw/sYP6TaFVVdtzcUJ7gvMppc=1
 
< 0.1%
Z3a78s0yd2RKBmzbbgZSL3hHHiFou4XHmgrto98NZu8=1
 
< 0.1%
wiRcrrJmtHIk2yF1dRiDYq+EI9womxMzPoJuxEW/jys=1
 
< 0.1%
qgeZDGVoBbyOgBRcWv9H8RoJ/XJTnSgoPw4+3Amuvx0=1
 
< 0.1%
C0Pz1O2940qoI4iHBIdQdZtYdTKn9u/8kJy3AAGhESc=1
 
< 0.1%
Other values (3230)3230
99.7%

Length

2023-05-19T11:00:38.956735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
t4tle/joip0/bg9kokr3rd19xvzdihlvzvs3dhpweik1
 
< 0.1%
oyyjgi3emtpjamxlbtrocylxr3spcb/4zxrqttqfm741
 
< 0.1%
6y0f6ly1axh1qai68f0faakjgu/dpp+j63xtnsmdtt41
 
< 0.1%
hsr0pfz3ijtzva17doxoqq20i4wfyfkln/wgt914vri1
 
< 0.1%
zqbqiwqgoypdp/cykgdc0tczfijjbkieeiu3/ygapmo1
 
< 0.1%
e/+uhebm49ngcppd/vaw/syp6tafvvdtzcuj7gvmppc1
 
< 0.1%
z3a78s0yd2rkbmzbbgzsl3hhhifou4xhmgrto98nzu81
 
< 0.1%
wircrrjmthik2yf1dridyq+ei9womxmzpojuxew/jys1
 
< 0.1%
qgezdgvobbyogbrcwv9h8roj/xjtnsgopw4+3amuvx01
 
< 0.1%
c0pz1o2940qoi4ihbidqdztydtkn9u/8kjy3aaghesc1
 
< 0.1%
Other values (3230)3230
99.7%

Most occurring characters

ValueCountFrequency (%)
=3240
 
2.3%
I2427
 
1.7%
02389
 
1.7%
A2370
 
1.7%
g2358
 
1.7%
s2351
 
1.6%
c2349
 
1.6%
82335
 
1.6%
Q2333
 
1.6%
E2333
 
1.6%
Other values (55)118075
82.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter56582
39.7%
Lowercase Letter56288
39.5%
Decimal Number22086
 
15.5%
Math Symbol5462
 
3.8%
Other Punctuation2142
 
1.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I2427
 
4.3%
A2370
 
4.2%
Q2333
 
4.1%
E2333
 
4.1%
M2319
 
4.1%
Y2313
 
4.1%
U2252
 
4.0%
W2170
 
3.8%
Z2169
 
3.8%
H2158
 
3.8%
Other values (16)33738
59.6%
Lowercase Letter
ValueCountFrequency (%)
g2358
 
4.2%
s2351
 
4.2%
c2349
 
4.2%
o2305
 
4.1%
w2294
 
4.1%
k2264
 
4.0%
m2225
 
4.0%
p2207
 
3.9%
b2167
 
3.8%
e2162
 
3.8%
Other values (16)33606
59.7%
Decimal Number
ValueCountFrequency (%)
02389
10.8%
82335
10.6%
42279
10.3%
52191
9.9%
22181
9.9%
62159
9.8%
32156
9.8%
92145
9.7%
12133
9.7%
72118
9.6%
Math Symbol
ValueCountFrequency (%)
=3240
59.3%
+2222
40.7%
Other Punctuation
ValueCountFrequency (%)
/2142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin112870
79.2%
Common29690
 
20.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
I2427
 
2.2%
A2370
 
2.1%
g2358
 
2.1%
s2351
 
2.1%
c2349
 
2.1%
Q2333
 
2.1%
E2333
 
2.1%
M2319
 
2.1%
Y2313
 
2.0%
o2305
 
2.0%
Other values (42)89412
79.2%
Common
ValueCountFrequency (%)
=3240
10.9%
02389
 
8.0%
82335
 
7.9%
42279
 
7.7%
+2222
 
7.5%
52191
 
7.4%
22181
 
7.3%
62159
 
7.3%
32156
 
7.3%
92145
 
7.2%
Other values (3)6393
21.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII142560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
=3240
 
2.3%
I2427
 
1.7%
02389
 
1.7%
A2370
 
1.7%
g2358
 
1.7%
s2351
 
1.6%
c2349
 
1.6%
82335
 
1.6%
Q2333
 
1.6%
E2333
 
1.6%
Other values (55)118075
82.8%

city
Real number (ℝ≥0)

HIGH CORRELATION

Distinct19
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.179012346
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2023-05-19T11:00:39.068383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile17
Maximum22
Range21
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.782679871
Coefficient of variation (CV)1.383743189
Kurtosis1.974860602
Mean4.179012346
Median Absolute Deviation (MAD)0
Skewness1.773794867
Sum13540
Variance33.43938649
MonotonicityNot monotonic
2023-05-19T11:00:39.166683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
12228
68.8%
13203
 
6.3%
5179
 
5.5%
4131
 
4.0%
22119
 
3.7%
15109
 
3.4%
655
 
1.7%
1454
 
1.7%
1121
 
0.6%
1821
 
0.6%
Other values (9)120
 
3.7%
ValueCountFrequency (%)
12228
68.8%
319
 
0.6%
4131
 
4.0%
5179
 
5.5%
655
 
1.7%
72
 
0.1%
816
 
0.5%
920
 
0.6%
1015
 
0.5%
1121
 
0.6%
ValueCountFrequency (%)
22119
3.7%
2117
 
0.5%
1821
 
0.6%
178
 
0.2%
162
 
0.1%
15109
3.4%
1454
 
1.7%
13203
6.3%
1221
 
0.6%
1121
 
0.6%

bd
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct48
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.23395062
Minimum6.3
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2023-05-19T11:00:39.283846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6.3
5-th percentile6.3
Q16.3
median6.3
Q319
95-th percentile34
Maximum64
Range57.7
Interquartile range (IQR)12.7

Descriptive statistics

Standard deviation10.34671505
Coefficient of variation (CV)0.8457378466
Kurtosis2.158231595
Mean12.23395062
Median Absolute Deviation (MAD)0
Skewness1.66947139
Sum39638
Variance107.0545123
MonotonicityNot monotonic
2023-05-19T11:00:39.422658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
6.32310
71.3%
2267
 
2.1%
2158
 
1.8%
2757
 
1.8%
2057
 
1.8%
2555
 
1.7%
2454
 
1.7%
2353
 
1.6%
2651
 
1.6%
1948
 
1.5%
Other values (38)430
 
13.3%
ValueCountFrequency (%)
6.32310
71.3%
71
 
< 0.1%
142
 
0.1%
153
 
0.1%
169
 
0.3%
1734
 
1.0%
1844
 
1.4%
1948
 
1.5%
2057
 
1.8%
2158
 
1.8%
ValueCountFrequency (%)
641
 
< 0.1%
611
 
< 0.1%
581
 
< 0.1%
572
 
0.1%
562
 
0.1%
542
 
0.1%
534
0.1%
525
0.2%
512
 
0.1%
504
0.1%

gender
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
2
2297 
0
502 
1
441 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3240
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
22297
70.9%
0502
 
15.5%
1441
 
13.6%

Length

2023-05-19T11:00:39.658204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-05-19T11:00:39.735143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
22297
70.9%
0502
 
15.5%
1441
 
13.6%

Most occurring characters

ValueCountFrequency (%)
22297
70.9%
0502
 
15.5%
1441
 
13.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
22297
70.9%
0502
 
15.5%
1441
 
13.6%

Most occurring scripts

ValueCountFrequency (%)
Common3240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
22297
70.9%
0502
 
15.5%
1441
 
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII3240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22297
70.9%
0502
 
15.5%
1441
 
13.6%

registered_via
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
7
2085 
4
461 
9
387 
3
281 
13
 
26

Length

Max length2
Median length1
Mean length1.008024691
Min length1

Characters and Unicode

Total characters3266
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row7
4th row7
5th row7

Common Values

ValueCountFrequency (%)
72085
64.4%
4461
 
14.2%
9387
 
11.9%
3281
 
8.7%
1326
 
0.8%

Length

2023-05-19T11:00:39.913610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-05-19T11:00:39.989206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
72085
64.4%
4461
 
14.2%
9387
 
11.9%
3281
 
8.7%
1326
 
0.8%

Most occurring characters

ValueCountFrequency (%)
72085
63.8%
4461
 
14.1%
9387
 
11.8%
3307
 
9.4%
126
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3266
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
72085
63.8%
4461
 
14.1%
9387
 
11.8%
3307
 
9.4%
126
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common3266
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
72085
63.8%
4461
 
14.1%
9387
 
11.8%
3307
 
9.4%
126
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3266
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
72085
63.8%
4461
 
14.1%
9387
 
11.8%
3307
 
9.4%
126
 
0.8%
Distinct729
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
Minimum2015-01-01 00:00:00
Maximum2017-02-07 00:00:00
2023-05-19T11:00:40.091418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:00:40.236275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

is_churn
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
0
3029 
1
 
211

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03029
93.5%
1211
 
6.5%

Length

2023-05-19T11:00:40.464669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-05-19T11:00:40.538989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
03029
93.5%
1211
 
6.5%

Most occurring characters

ValueCountFrequency (%)
03029
93.5%
1211
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03029
93.5%
1211
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
Common3240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03029
93.5%
1211
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII3240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03029
93.5%
1211
 
6.5%

total_payment_channels
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.79444444
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2023-05-19T11:00:40.614052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q17
median12
Q317
95-th percentile22
Maximum36
Range35
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.115061702
Coefficient of variation (CV)0.5184696686
Kurtosis-0.7101382253
Mean11.79444444
Median Absolute Deviation (MAD)5
Skewness0.2016205715
Sum38214
Variance37.39397962
MonotonicityNot monotonic
2023-05-19T11:00:40.729293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
8212
 
6.5%
17207
 
6.4%
14207
 
6.4%
9201
 
6.2%
13176
 
5.4%
16175
 
5.4%
7173
 
5.3%
3167
 
5.2%
18162
 
5.0%
12157
 
4.8%
Other values (21)1403
43.3%
ValueCountFrequency (%)
138
 
1.2%
2120
3.7%
3167
5.2%
4147
4.5%
5132
4.1%
6139
4.3%
7173
5.3%
8212
6.5%
9201
6.2%
10133
4.1%
ValueCountFrequency (%)
361
 
< 0.1%
322
 
0.1%
292
 
0.1%
282
 
0.1%
2710
 
0.3%
2620
0.6%
2524
0.7%
2442
1.3%
2339
1.2%
2234
1.0%

change_in_payment_methods
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
1
2843 
2
326 
3
 
67
4
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3240
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
12843
87.7%
2326
 
10.1%
367
 
2.1%
44
 
0.1%

Length

2023-05-19T11:00:40.962880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-05-19T11:00:41.032494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
12843
87.7%
2326
 
10.1%
367
 
2.1%
44
 
0.1%

Most occurring characters

ValueCountFrequency (%)
12843
87.7%
2326
 
10.1%
367
 
2.1%
44
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
12843
87.7%
2326
 
10.1%
367
 
2.1%
44
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common3240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
12843
87.7%
2326
 
10.1%
367
 
2.1%
44
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12843
87.7%
2326
 
10.1%
367
 
2.1%
44
 
0.1%

payment_plan_days_mean
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct190
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.6291838
Minimum14.55555556
Maximum450
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2023-05-19T11:00:41.130340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum14.55555556
5-th percentile28.63636364
Q130
median30
Q330
95-th percentile35.16043956
Maximum450
Range435.4444444
Interquartile range (IQR)0

Descriptive statistics

Standard deviation28.03768047
Coefficient of variation (CV)0.8337306262
Kurtosis128.6321562
Mean33.6291838
Median Absolute Deviation (MAD)0
Skewness10.63379202
Sum108958.5555
Variance786.111526
MonotonicityNot monotonic
2023-05-19T11:00:41.276435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
302783
85.9%
28.7521
 
0.6%
24.2518
 
0.6%
28.6956521718
 
0.6%
28.6363636414
 
0.4%
28.811
 
0.3%
4511
 
0.3%
25.411
 
0.3%
41010
 
0.3%
28.846153858
 
0.2%
Other values (180)335
 
10.3%
ValueCountFrequency (%)
14.555555561
 
< 0.1%
18.52
 
0.1%
20.142857141
 
< 0.1%
20.81
 
< 0.1%
21.818181821
 
< 0.1%
22.173913043
 
0.1%
22.333333335
 
0.2%
23.181818182
 
0.1%
24.2518
0.6%
24.285714291
 
< 0.1%
ValueCountFrequency (%)
4501
 
< 0.1%
41010
0.3%
377.51
 
< 0.1%
2651
 
< 0.1%
2401
 
< 0.1%
2202
 
0.1%
2051
 
< 0.1%
2003
 
0.1%
1957
0.2%
1803
 
0.1%

change_in_plan
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
1
2818 
2
344 
3
 
72
4
 
5
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3240
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
12818
87.0%
2344
 
10.6%
372
 
2.2%
45
 
0.2%
51
 
< 0.1%

Length

2023-05-19T11:00:41.508103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-05-19T11:00:41.587214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
12818
87.0%
2344
 
10.6%
372
 
2.2%
45
 
0.2%
51
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
12818
87.0%
2344
 
10.6%
372
 
2.2%
45
 
0.2%
51
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
12818
87.0%
2344
 
10.6%
372
 
2.2%
45
 
0.2%
51
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common3240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
12818
87.0%
2344
 
10.6%
372
 
2.2%
45
 
0.2%
51
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12818
87.0%
2344
 
10.6%
372
 
2.2%
45
 
0.2%
51
 
< 0.1%

plan_list_price_mean
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct474
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.228954
Minimum0
Maximum1788
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2023-05-19T11:00:41.688802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile99
Q199
median110.1304348
Q3149
95-th percentile180
Maximum1788
Range1788
Interquartile range (IQR)50

Descriptive statistics

Standard deviation119.4715099
Coefficient of variation (CV)0.858093855
Kurtosis122.3844121
Mean139.228954
Median Absolute Deviation (MAD)11.13043478
Skewness10.04625679
Sum451101.811
Variance14273.44168
MonotonicityNot monotonic
2023-05-19T11:00:41.826883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
991502
46.4%
149658
20.3%
180148
 
4.6%
12950
 
1.5%
10030
 
0.9%
142.791666722
 
0.7%
143.0413
 
0.4%
142.227272711
 
0.3%
111.7511
 
0.3%
15011
 
0.3%
Other values (464)784
24.2%
ValueCountFrequency (%)
01
< 0.1%
53.333333331
< 0.1%
74.51
< 0.1%
751
< 0.1%
85.142857141
< 0.1%
89.41
< 0.1%
901
< 0.1%
92.8751
< 0.1%
93.176470591
< 0.1%
941
< 0.1%
ValueCountFrequency (%)
178810
0.3%
1399.51
 
< 0.1%
1132.51
 
< 0.1%
9841
 
< 0.1%
958.51
 
< 0.1%
9303
 
0.1%
8946
0.2%
799.51
 
< 0.1%
7161
 
< 0.1%
695.33333331
 
< 0.1%

actual_amount_paid_mean
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct492
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.4574094
Minimum0
Maximum1788
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2023-05-19T11:00:41.987257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile99
Q199
median102.3666667
Q3149
95-th percentile180
Maximum1788
Range1788
Interquartile range (IQR)50

Descriptive statistics

Standard deviation119.6452441
Coefficient of variation (CV)0.8641303095
Kurtosis121.8451475
Mean138.4574094
Median Absolute Deviation (MAD)12.58525641
Skewness10.01005139
Sum448602.0066
Variance14314.98443
MonotonicityNot monotonic
2023-05-19T11:00:42.126141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
991502
46.4%
149671
20.7%
180147
 
4.5%
12951
 
1.6%
10030
 
0.9%
111.7514
 
0.4%
15011
 
0.3%
132.444444411
 
0.3%
178810
 
0.3%
1759
 
0.3%
Other values (482)784
24.2%
ValueCountFrequency (%)
01
 
< 0.1%
49.666666671
 
< 0.1%
53.333333331
 
< 0.1%
59.61
 
< 0.1%
601
 
< 0.1%
64.51
 
< 0.1%
662
 
0.1%
74.254
0.1%
74.55
0.2%
751
 
< 0.1%
ValueCountFrequency (%)
178810
0.3%
1399.51
 
< 0.1%
1132.51
 
< 0.1%
9841
 
< 0.1%
958.51
 
< 0.1%
9303
 
0.1%
8946
0.2%
799.51
 
< 0.1%
7161
 
< 0.1%
6901
 
< 0.1%

is_auto_renew_mean
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct78
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8247552119
Minimum0
Maximum1
Zeros487
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2023-05-19T11:00:42.280695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3631098106
Coefficient of variation (CV)0.4402637356
Kurtosis1.140655052
Mean0.8247552119
Median Absolute Deviation (MAD)0
Skewness-1.734857511
Sum2672.206887
Variance0.1318487345
MonotonicityNot monotonic
2023-05-19T11:00:42.431328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12513
77.6%
0487
 
15.0%
0.7520
 
0.6%
0.666666666717
 
0.5%
0.514
 
0.4%
0.888888888910
 
0.3%
0.85714285719
 
0.3%
0.99
 
0.3%
0.89
 
0.3%
0.8759
 
0.3%
Other values (68)143
 
4.4%
ValueCountFrequency (%)
0487
15.0%
0.051
 
< 0.1%
0.083333333331
 
< 0.1%
0.13333333331
 
< 0.1%
0.15384615381
 
< 0.1%
0.16666666675
 
0.2%
0.18181818181
 
< 0.1%
0.18751
 
< 0.1%
0.22
 
0.1%
0.21428571431
 
< 0.1%
ValueCountFrequency (%)
12513
77.6%
0.95833333331
 
< 0.1%
0.95454545451
 
< 0.1%
0.95238095241
 
< 0.1%
0.952
 
0.1%
0.94736842112
 
0.1%
0.94444444441
 
< 0.1%
0.94117647064
 
0.1%
0.93753
 
0.1%
0.92857142862
 
0.1%

is_autorenew_change_flag
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
1
2753 
0
487 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
12753
85.0%
0487
 
15.0%

Length

2023-05-19T11:00:42.647088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-05-19T11:00:42.721090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
12753
85.0%
0487
 
15.0%

Most occurring characters

ValueCountFrequency (%)
12753
85.0%
0487
 
15.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
12753
85.0%
0487
 
15.0%

Most occurring scripts

ValueCountFrequency (%)
Common3240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
12753
85.0%
0487
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12753
85.0%
0487
 
15.0%
Distinct721
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
Minimum2015-01-01 00:00:00
Maximum2017-02-17 00:00:00
2023-05-19T11:00:42.808989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:00:42.947017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct121
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
Minimum2015-11-24 00:00:00
Maximum2017-02-28 00:00:00
2023-05-19T11:00:43.113154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:00:43.258366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

total_transactions
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.79444444
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2023-05-19T11:00:43.408993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q17
median12
Q317
95-th percentile22
Maximum36
Range35
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.115061702
Coefficient of variation (CV)0.5184696686
Kurtosis-0.7101382253
Mean11.79444444
Median Absolute Deviation (MAD)5
Skewness0.2016205715
Sum38214
Variance37.39397962
MonotonicityNot monotonic
2023-05-19T11:00:43.533014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
8212
 
6.5%
17207
 
6.4%
14207
 
6.4%
9201
 
6.2%
13176
 
5.4%
16175
 
5.4%
7173
 
5.3%
3167
 
5.2%
18162
 
5.0%
12157
 
4.8%
Other values (21)1403
43.3%
ValueCountFrequency (%)
138
 
1.2%
2120
3.7%
3167
5.2%
4147
4.5%
5132
4.1%
6139
4.3%
7173
5.3%
8212
6.5%
9201
6.2%
10133
4.1%
ValueCountFrequency (%)
361
 
< 0.1%
322
 
0.1%
292
 
0.1%
282
 
0.1%
2710
 
0.3%
2620
0.6%
2524
0.7%
2442
1.3%
2339
1.2%
2234
1.0%

membership_expire_date_max
Date

HIGH CORRELATION

Distinct59
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
Minimum2017-02-01 00:00:00
Maximum2017-03-31 00:00:00
2023-05-19T11:00:43.668370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:00:43.825871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

is_cancel_mean
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct46
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01397242557
Minimum0
Maximum0.5
Zeros2770
Zeros (%)85.5%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2023-05-19T11:00:43.963130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.09090909091
Maximum0.5
Range0.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.04222331276
Coefficient of variation (CV)3.021902857
Kurtosis26.4125793
Mean0.01397242557
Median Absolute Deviation (MAD)0
Skewness4.462107264
Sum45.27065886
Variance0.001782808141
MonotonicityNot monotonic
2023-05-19T11:00:44.106529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
02770
85.5%
0.0555555555633
 
1.0%
0.0666666666733
 
1.0%
0.0714285714332
 
1.0%
0.062526
 
0.8%
0.0588235294125
 
0.8%
0.111111111123
 
0.7%
0.0909090909123
 
0.7%
0.123
 
0.7%
0.0769230769222
 
0.7%
Other values (36)230
 
7.1%
ValueCountFrequency (%)
02770
85.5%
0.034482758621
 
< 0.1%
0.035714285711
 
< 0.1%
0.037037037046
 
0.2%
0.038461538466
 
0.2%
0.047
 
0.2%
0.041666666676
 
0.2%
0.0434782608714
 
0.4%
0.045454545455
 
0.2%
0.0476190476217
 
0.5%
ValueCountFrequency (%)
0.51
 
< 0.1%
0.42
 
0.1%
0.333333333311
0.3%
0.29166666671
 
< 0.1%
0.2511
0.3%
0.23809523811
 
< 0.1%
0.22222222222
 
0.1%
0.211
0.3%
0.18181818181
 
< 0.1%
0.166666666720
0.6%

is_cancel_change_flag
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
0
2770 
1
470 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
02770
85.5%
1470
 
14.5%

Length

2023-05-19T11:00:44.324170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-05-19T11:00:44.391599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
02770
85.5%
1470
 
14.5%

Most occurring characters

ValueCountFrequency (%)
02770
85.5%
1470
 
14.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02770
85.5%
1470
 
14.5%

Most occurring scripts

ValueCountFrequency (%)
Common3240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02770
85.5%
1470
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII3240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02770
85.5%
1470
 
14.5%

discount_mean
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct95
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7715445879
Minimum-223.5
Maximum120
Zeros2911
Zeros (%)89.8%
Negative136
Negative (%)4.2%
Memory size25.4 KiB
2023-05-19T11:00:44.481447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-223.5
5-th percentile0
Q10
median0
Q30
95-th percentile8.277777778
Maximum120
Range343.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.732987399
Coefficient of variation (CV)11.31883696
Kurtosis220.6410199
Mean0.7715445879
Median Absolute Deviation (MAD)0
Skewness-4.329118954
Sum2499.804465
Variance76.26506891
MonotonicityNot monotonic
2023-05-19T11:00:44.627918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02911
89.8%
-6.20833333318
 
0.6%
-5.9615
 
0.5%
18.62514
 
0.4%
-6.4782608713
 
0.4%
16.5555555613
 
0.4%
14.910
 
0.3%
24.833333339
 
0.3%
9.31259
 
0.3%
49.666666679
 
0.3%
Other values (85)219
 
6.8%
ValueCountFrequency (%)
-223.51
< 0.1%
-178.81
< 0.1%
-40.636363641
< 0.1%
-38.869565221
< 0.1%
-37.565217392
0.1%
-33.863636361
< 0.1%
-32.954545451
< 0.1%
-28.380952381
< 0.1%
-22.352
0.1%
-21.428571431
< 0.1%
ValueCountFrequency (%)
1201
 
< 0.1%
99.333333331
 
< 0.1%
89.41
 
< 0.1%
74.55
0.2%
59.62
 
0.1%
49.666666679
0.3%
42.571428571
 
< 0.1%
37.259
0.3%
35.058823531
 
< 0.1%
33.111111111
 
< 0.1%

is_discount_mean
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct48
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.009980551507
Minimum0
Maximum1
Zeros3026
Zeros (%)93.4%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2023-05-19T11:00:44.770848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.05882352941
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.05208919576
Coefficient of variation (CV)5.21906988
Kurtosis100.3478214
Mean0.009980551507
Median Absolute Deviation (MAD)0
Skewness8.62887225
Sum32.33698688
Variance0.002713284315
MonotonicityNot monotonic
2023-05-19T11:00:44.916888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
03026
93.4%
0.12514
 
0.4%
0.111111111113
 
0.4%
0.111
 
0.3%
0.0526315789510
 
0.3%
0.06259
 
0.3%
0.055555555569
 
0.3%
0.29
 
0.3%
0.259
 
0.3%
0.33333333339
 
0.3%
Other values (38)121
 
3.7%
ValueCountFrequency (%)
03026
93.4%
0.037037037042
 
0.1%
0.038461538464
 
0.1%
0.041
 
< 0.1%
0.041666666675
 
0.2%
0.043478260873
 
0.1%
0.045454545455
 
0.2%
0.047619047625
 
0.2%
0.053
 
0.1%
0.0526315789510
 
0.3%
ValueCountFrequency (%)
11
 
< 0.1%
0.77777777781
 
< 0.1%
0.66666666672
 
0.1%
0.61
 
< 0.1%
0.52941176471
 
< 0.1%
0.55
0.2%
0.46153846151
 
< 0.1%
0.42
 
0.1%
0.34615384622
 
0.1%
0.33333333339
0.3%

is_discount_max
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
0
3026 
1
 
214

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03026
93.4%
1214
 
6.6%

Length

2023-05-19T11:00:45.140488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-05-19T11:00:45.208037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
03026
93.4%
1214
 
6.6%

Most occurring characters

ValueCountFrequency (%)
03026
93.4%
1214
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03026
93.4%
1214
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
Common3240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03026
93.4%
1214
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII3240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03026
93.4%
1214
 
6.6%

amt_per_day_mean
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct535
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.072860313
Minimum0
Maximum6
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2023-05-19T11:00:45.296575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.3
Q13.3
median3.333333333
Q34.966666667
95-th percentile5.846153846
Maximum6
Range6
Interquartile range (IQR)1.666666667

Descriptive statistics

Standard deviation0.9099726283
Coefficient of variation (CV)0.2234234809
Kurtosis-0.9085713907
Mean4.072860313
Median Absolute Deviation (MAD)0.3622222222
Skewness0.455419139
Sum13196.06741
Variance0.8280501843
MonotonicityNot monotonic
2023-05-19T11:00:45.445134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.31318
40.7%
4.966666667584
18.0%
3.3184
 
5.7%
6147
 
4.5%
4.351
 
1.6%
3.33333333331
 
1.0%
4.75972222219
 
0.6%
3.72514
 
0.4%
512
 
0.4%
4.41481481512
 
0.4%
Other values (525)868
26.8%
ValueCountFrequency (%)
01
< 0.1%
1.3244444441
< 0.1%
1.6555555561
< 0.1%
1.7777777781
< 0.1%
1.9866666671
< 0.1%
1.98751
< 0.1%
1.998751
< 0.1%
21
< 0.1%
2.151
< 0.1%
2.1804878051
< 0.1%
ValueCountFrequency (%)
6147
4.5%
5.9166666675
 
0.2%
5.93
 
0.1%
5.8751
 
< 0.1%
5.8523809522
 
0.1%
5.8509977831
 
< 0.1%
5.851
 
< 0.1%
5.8461538464
 
0.1%
5.8333333339
 
0.3%
5.8305555561
 
< 0.1%

membership_duration_mean
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct729
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.58014865
Minimum-2395
Maximum450
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size25.4 KiB
2023-05-19T11:00:45.586537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-2395
5-th percentile27.25
Q129.82954545
median30.3125
Q330.41666667
95-th percentile45.87958333
Maximum450
Range2845
Interquartile range (IQR)0.5871212121

Descriptive statistics

Standard deviation51.21829634
Coefficient of variation (CV)1.5252552
Kurtosis1572.548189
Mean33.58014865
Median Absolute Deviation (MAD)0.3125
Skewness-31.16723676
Sum108799.6816
Variance2623.31388
MonotonicityNot monotonic
2023-05-19T11:00:45.727434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30271
 
8.4%
30.375141
 
4.4%
30.33333333139
 
4.3%
30.499
 
3.1%
30.2857142993
 
2.9%
30.3571428679
 
2.4%
30.2572
 
2.2%
30.4117647169
 
2.1%
30.3529411862
 
1.9%
30.3076923161
 
1.9%
Other values (719)2154
66.5%
ValueCountFrequency (%)
-23951
< 0.1%
31
< 0.1%
6.3333333331
< 0.1%
11.666666671
< 0.1%
121
< 0.1%
151
< 0.1%
15.51
< 0.1%
17.41
< 0.1%
181
< 0.1%
191
< 0.1%
ValueCountFrequency (%)
4501
 
< 0.1%
4341
 
< 0.1%
4161
 
< 0.1%
4134
0.1%
4121
 
< 0.1%
4111
 
< 0.1%
4101
 
< 0.1%
3802
0.1%
2661
 
< 0.1%
221.51
 
< 0.1%

more_than_30_sum
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct25
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.60462963
Minimum0
Maximum24
Zeros428
Zeros (%)13.2%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2023-05-19T11:00:45.858766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q39
95-th percentile12
Maximum24
Range24
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.146906008
Coefficient of variation (CV)0.7399072342
Kurtosis-0.1578219444
Mean5.60462963
Median Absolute Deviation (MAD)4
Skewness0.4385603041
Sum18159
Variance17.19682944
MonotonicityNot monotonic
2023-05-19T11:00:45.973933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0428
13.2%
1307
9.5%
9301
9.3%
7290
9.0%
5268
8.3%
2248
7.7%
10234
7.2%
6207
 
6.4%
3207
 
6.4%
8202
 
6.2%
Other values (15)548
16.9%
ValueCountFrequency (%)
0428
13.2%
1307
9.5%
2248
7.7%
3207
6.4%
4171
 
5.3%
5268
8.3%
6207
6.4%
7290
9.0%
8202
6.2%
9301
9.3%
ValueCountFrequency (%)
241
 
< 0.1%
232
 
0.1%
224
0.1%
212
 
0.1%
202
 
0.1%
195
0.2%
186
0.2%
178
0.2%
163
 
0.1%
157
0.2%

num_25
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2496
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.064854503
Minimum0
Maximum5.087596416
Zeros134
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2023-05-19T11:00:46.111081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1155245304
Q10.5493061543
median0.9633517265
Q31.469801635
95-th percentile2.369483924
Maximum5.087596416
Range5.087596416
Interquartile range (IQR)0.9204954803

Descriptive statistics

Standard deviation0.7031103969
Coefficient of variation (CV)0.6602877378
Kurtosis1.282059669
Mean1.064854503
Median Absolute Deviation (MAD)0.4530927241
Skewness0.9175319672
Sum3450.128662
Variance0.4943642318
MonotonicityNot monotonic
2023-05-19T11:00:46.258430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0134
 
4.1%
0.173286795653
 
1.6%
0.346573591234
 
1.0%
0.447939872722
 
0.7%
0.274653077122
 
0.7%
0.231049060819
 
0.6%
0.621226668414
 
0.4%
0.549306154313
 
0.4%
0.79451346412
 
0.4%
0.519860386811
 
0.3%
Other values (2486)2906
89.7%
ValueCountFrequency (%)
0134
4.1%
0.04332169894
 
0.1%
0.057762265212
 
0.1%
0.077016353612
 
0.1%
0.086643397817
 
0.2%
0.091551028193
 
0.1%
0.096270442011
 
< 0.1%
0.0990210251
 
< 0.1%
0.10058987141
 
< 0.1%
0.10322756321
 
< 0.1%
ValueCountFrequency (%)
5.0875964161
< 0.1%
4.7791233061
< 0.1%
4.6689410211
< 0.1%
4.4318809511
< 0.1%
4.0943446161
< 0.1%
4.0048747061
< 0.1%
3.9345114231
< 0.1%
3.8470385071
< 0.1%
3.7391512391
< 0.1%
3.6061472891
< 0.1%

num_50
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1468
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3602947593
Minimum0
Maximum4.204692841
Zeros556
Zeros (%)17.2%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2023-05-19T11:00:46.397643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.08958797157
median0.2735635042
Q30.5163983405
95-th percentile1.069166541
Maximum4.204692841
Range4.204692841
Interquartile range (IQR)0.4268103689

Descriptive statistics

Standard deviation0.3708899021
Coefficient of variation (CV)1.029406905
Kurtosis8.441922188
Mean0.3602947593
Median Absolute Deviation (MAD)0.1988659501
Skewness2.131618261
Sum1167.35498
Variance0.1375593096
MonotonicityNot monotonic
2023-05-19T11:00:46.543418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0556
 
17.2%
0.1732867956131
 
4.0%
0.0866433978167
 
2.1%
0.346573591249
 
1.5%
0.0577622652148
 
1.5%
0.274653077142
 
1.3%
0.115524530435
 
1.1%
0.231049060833
 
1.0%
0.447939872730
 
0.9%
0.310613334225
 
0.8%
Other values (1458)2224
68.6%
ValueCountFrequency (%)
0556
17.2%
0.015753345561
 
< 0.1%
0.021660849452
 
0.1%
0.024755256253
 
0.1%
0.025672117251
 
< 0.1%
0.02665950731
 
< 0.1%
0.02718224191
 
< 0.1%
0.02888113262
 
0.1%
0.030136834831
 
< 0.1%
0.031506691131
 
< 0.1%
ValueCountFrequency (%)
4.2046928411
< 0.1%
2.8332133291
< 0.1%
2.7925384041
< 0.1%
2.772588731
< 0.1%
2.3750867841
< 0.1%
2.282174111
< 0.1%
2.2529194361
< 0.1%
2.1972246171
< 0.1%
2.1941189771
< 0.1%
2.1930205821
< 0.1%

num_75
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1060
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2255322188
Minimum0
Maximum2.302585125
Zeros844
Zeros (%)26.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2023-05-19T11:00:46.685348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.1625835672
Q30.3219796717
95-th percentile0.7225929499
Maximum2.302585125
Range2.302585125
Interquartile range (IQR)0.3219796717

Descriptive statistics

Standard deviation0.2635063827
Coefficient of variation (CV)1.168375731
Kurtosis8.076948166
Mean0.2255322188
Median Absolute Deviation (MAD)0.1625835598
Skewness2.251515627
Sum730.7243652
Variance0.06943561882
MonotonicityNot monotonic
2023-05-19T11:00:46.821334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0844
26.0%
0.1732867956142
 
4.4%
0.0866433978194
 
2.9%
0.0577622652163
 
1.9%
0.231049060848
 
1.5%
0.346573591244
 
1.4%
0.115524530444
 
1.4%
0.274653077131
 
1.0%
0.149313285930
 
0.9%
0.223969936430
 
0.9%
Other values (1050)1870
57.7%
ValueCountFrequency (%)
0844
26.0%
0.017328679561
 
< 0.1%
0.01925408841
 
< 0.1%
0.019804205751
 
< 0.1%
0.023104906081
 
< 0.1%
0.024755256253
 
0.1%
0.02665950731
 
< 0.1%
0.027725886551
 
< 0.1%
0.02888113268
 
0.2%
0.030517008161
 
< 0.1%
ValueCountFrequency (%)
2.3025851251
< 0.1%
2.1972246171
< 0.1%
2.1440541741
< 0.1%
1.9459102151
< 0.1%
1.9356005191
< 0.1%
1.9128103261
< 0.1%
1.836059571
< 0.1%
1.7917594911
< 0.1%
1.7524985071
< 0.1%
1.666102291
< 0.1%

num_985
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1082
Distinct (%)33.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2363214344
Minimum0
Maximum2.224787235
Zeros815
Zeros (%)25.2%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2023-05-19T11:00:46.973929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.1732867956
Q30.3343475312
95-th percentile0.7880127043
Maximum2.224787235
Range2.224787235
Interquartile range (IQR)0.3343475312

Descriptive statistics

Standard deviation0.2739073336
Coefficient of variation (CV)1.159045696
Kurtosis6.389976501
Mean0.2363214344
Median Absolute Deviation (MAD)0.1629528999
Skewness2.102514029
Sum765.6814575
Variance0.07502523065
MonotonicityNot monotonic
2023-05-19T11:00:47.106615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0815
25.2%
0.1732867956152
 
4.7%
0.0866433978190
 
2.8%
0.0577622652160
 
1.9%
0.115524530454
 
1.7%
0.274653077145
 
1.4%
0.231049060841
 
1.3%
0.346573591240
 
1.2%
0.137326538627
 
0.8%
0.0915510281923
 
0.7%
Other values (1072)1893
58.4%
ValueCountFrequency (%)
0815
25.2%
0.015753345561
 
< 0.1%
0.017328679561
 
< 0.1%
0.01925408842
 
0.1%
0.021660849453
 
0.1%
0.024755256256
 
0.2%
0.028881132611
 
0.3%
0.030136834831
 
< 0.1%
0.030517008161
 
< 0.1%
0.031388923531
 
< 0.1%
ValueCountFrequency (%)
2.2247872351
< 0.1%
2.1898386481
< 0.1%
2.0206606391
< 0.1%
1.7917594911
< 0.1%
1.7541589741
< 0.1%
1.7045514581
< 0.1%
1.6958655121
< 0.1%
1.6917246581
< 0.1%
1.6783990861
< 0.1%
1.6120940451
< 0.1%

num_100
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct3035
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.408321619
Minimum0
Maximum5.905887604
Zeros41
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2023-05-19T11:00:48.378318image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.8664339781
Q11.884092122
median2.439326286
Q32.966488123
95-th percentile3.773670042
Maximum5.905887604
Range5.905887604
Interquartile range (IQR)1.082396001

Descriptive statistics

Standard deviation0.8759378195
Coefficient of variation (CV)0.3637129664
Kurtosis0.4219321609
Mean2.408321619
Median Absolute Deviation (MAD)0.5415901542
Skewness-0.139826104
Sum7802.961914
Variance0.767267108
MonotonicityNot monotonic
2023-05-19T11:00:48.515804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
041
 
1.3%
0.693147182512
 
0.4%
0.34657359128
 
0.2%
0.51986038687
 
0.2%
2.0794415476
 
0.2%
0.89587974556
 
0.2%
0.54930615435
 
0.2%
0.86643397815
 
0.2%
1.4715260275
 
0.2%
1.3438196185
 
0.2%
Other values (3025)3140
96.9%
ValueCountFrequency (%)
041
1.3%
0.13732653861
 
< 0.1%
0.17328679564
 
0.1%
0.23104906082
 
0.1%
0.24849066141
 
< 0.1%
0.27465307713
 
0.1%
0.29862657191
 
< 0.1%
0.34657359128
 
0.2%
0.36620411282
 
0.1%
0.38918203121
 
< 0.1%
ValueCountFrequency (%)
5.9058876041
< 0.1%
5.3315830231
< 0.1%
5.1800260541
< 0.1%
5.0681772231
< 0.1%
5.0646114351
< 0.1%
5.0618114471
< 0.1%
5.0274214741
< 0.1%
5.019047261
< 0.1%
4.9757623671
< 0.1%
4.9126172071
< 0.1%

num_unq
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3107
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.625793695
Minimum0
Maximum5.407171726
Zeros11
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2023-05-19T11:00:48.671026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.328061116
Q12.183980525
median2.661792874
Q33.111875474
95-th percentile3.781433177
Maximum5.407171726
Range5.407171726
Interquartile range (IQR)0.9278949499

Descriptive statistics

Standard deviation0.7468746305
Coefficient of variation (CV)0.2844376564
Kurtosis0.7164651752
Mean2.625793695
Median Absolute Deviation (MAD)0.4640641212
Skewness-0.2901935875
Sum8507.571289
Variance0.557821691
MonotonicityNot monotonic
2023-05-19T11:00:48.814928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
011
 
0.3%
0.69314718258
 
0.2%
1.7917594918
 
0.2%
0.34657359127
 
0.2%
1.0986123097
 
0.2%
2.3025851255
 
0.2%
1.0397207745
 
0.2%
1.0593513254
 
0.1%
0.62122666844
 
0.1%
1.9022176274
 
0.1%
Other values (3097)3177
98.1%
ValueCountFrequency (%)
011
0.3%
0.17328679562
 
0.1%
0.27465307711
 
< 0.1%
0.34657359127
0.2%
0.36620411281
 
< 0.1%
0.43152609471
 
< 0.1%
0.44793987272
 
0.1%
0.46209812162
 
0.1%
0.48647755381
 
< 0.1%
0.52967566251
 
< 0.1%
ValueCountFrequency (%)
5.4071717261
< 0.1%
5.1474943161
< 0.1%
5.0025992391
< 0.1%
4.9081726071
< 0.1%
4.8653202061
< 0.1%
4.849166871
< 0.1%
4.8150234221
< 0.1%
4.7828025821
< 0.1%
4.7538280491
< 0.1%
4.7335605621
< 0.1%

total_secs
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct725
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.03125
Minimum2.455078125
Maximum10.7734375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-05-19T11:00:48.958158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.455078125
5-th percentile6.601367188
Q17.56640625
median8.0625
Q38.580078125
95-th percentile9.328515625
Maximum10.7734375
Range8.318359375
Interquartile range (IQR)1.013671875

Descriptive statistics

Standard deviation0.87890625
Coefficient of variation (CV)0.1094360352
Kurtosis2.97265625
Mean8.03125
Median Absolute Deviation (MAD)0.5078125
Skewness-0.7622070312
Sum26016
Variance0.7724609375
MonotonicityNot monotonic
2023-05-19T11:00:49.099336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.01562519
 
0.6%
8.23437518
 
0.6%
8.062518
 
0.6%
8.17187518
 
0.6%
8.679687517
 
0.5%
8.12517
 
0.5%
8.14062517
 
0.5%
8.335937517
 
0.5%
8.39062517
 
0.5%
8.054687517
 
0.5%
Other values (715)3065
94.6%
ValueCountFrequency (%)
2.4550781251
< 0.1%
2.8496093751
< 0.1%
2.8613281251
< 0.1%
2.97656251
< 0.1%
3.035156251
< 0.1%
3.82031251
< 0.1%
4.07031251
< 0.1%
4.089843751
< 0.1%
4.214843751
< 0.1%
4.363281251
< 0.1%
ValueCountFrequency (%)
10.77343751
< 0.1%
10.5781251
< 0.1%
10.5468751
< 0.1%
10.53906251
< 0.1%
10.52343751
< 0.1%
10.47656251
< 0.1%
10.46093751
< 0.1%
10.406251
< 0.1%
10.3906251
< 0.1%
10.3751
< 0.1%

login_freq
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.2
Minimum1
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2023-05-19T11:00:49.234417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q16
median12
Q321
95-th percentile36.05
Maximum60
Range59
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.47972157
Coefficient of variation (CV)0.7552448404
Kurtosis0.6164804356
Mean15.2
Median Absolute Deviation (MAD)8
Skewness1.05163755
Sum49248
Variance131.7840074
MonotonicityNot monotonic
2023-05-19T11:00:49.376519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4522
16.1%
8389
12.0%
12382
11.8%
16238
 
7.3%
20200
 
6.2%
24197
 
6.1%
28151
 
4.7%
3146
 
4.5%
32117
 
3.6%
698
 
3.0%
Other values (43)800
24.7%
ValueCountFrequency (%)
150
 
1.5%
249
 
1.5%
3146
 
4.5%
4522
16.1%
530
 
0.9%
698
 
3.0%
720
 
0.6%
8389
12.0%
971
 
2.2%
1015
 
0.5%
ValueCountFrequency (%)
602
 
0.1%
567
 
0.2%
541
 
< 0.1%
5218
0.6%
511
 
< 0.1%
492
 
0.1%
4826
0.8%
473
 
0.1%
451
 
< 0.1%
4439
1.2%
Distinct368
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
Minimum2015-02-19 00:00:00
Maximum2017-02-28 00:00:00
2023-05-19T11:00:49.522554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:00:49.654813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

registration_duration
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct629
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean405.6981481
Minimum22
Maximum819
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2023-05-19T11:00:49.811969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile90
Q1243
median424
Q3547
95-th percentile728.05
Maximum819
Range797
Interquartile range (IQR)304

Descriptive statistics

Standard deviation187.5153054
Coefficient of variation (CV)0.4622039963
Kurtosis-0.7962752704
Mean405.6981481
Median Absolute Deviation (MAD)149
Skewness-0.01974052938
Sum1314462
Variance35161.98975
MonotonicityNot monotonic
2023-05-19T11:00:49.956083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42580
 
2.5%
51779
 
2.4%
51669
 
2.1%
48668
 
2.1%
39466
 
2.0%
54762
 
1.9%
24261
 
1.9%
42457
 
1.8%
21156
 
1.7%
57856
 
1.7%
Other values (619)2586
79.8%
ValueCountFrequency (%)
221
 
< 0.1%
271
 
< 0.1%
304
 
0.1%
311
 
< 0.1%
331
 
< 0.1%
341
 
< 0.1%
351
 
< 0.1%
561
 
< 0.1%
5831
1.0%
595
 
0.2%
ValueCountFrequency (%)
8191
 
< 0.1%
8141
 
< 0.1%
8131
 
< 0.1%
8103
0.1%
8091
 
< 0.1%
8061
 
< 0.1%
8052
0.1%
8032
0.1%
8011
 
< 0.1%
7993
0.1%

Interactions

2023-05-19T10:59:23.645150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T10:59:23.778008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T10:59:23.916703image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T10:59:24.041586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T10:59:24.166391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T10:59:24.278650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T10:59:24.410825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T10:59:24.535164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T10:59:24.651304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T10:59:24.780125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T10:59:24.900923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T10:59:25.018639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T10:59:25.141532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T10:59:25.269171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T10:59:25.387552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T10:59:25.506641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T10:59:25.628285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T10:59:25.738621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T10:59:25.854632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T10:59:25.980440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T10:59:26.096251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T10:59:26.210110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T10:59:26.327509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T10:59:26.442506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T10:59:26.566414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T10:59:26.718087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T10:59:26.867638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T10:59:27.013578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T10:59:27.159143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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Correlations

2023-05-19T11:00:50.129828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-05-19T11:00:50.442474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-05-19T11:00:50.757633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-05-19T11:00:51.076788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2023-05-19T11:00:51.367129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2023-05-19T11:00:37.249382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-19T11:00:38.212404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexmsnocitybdgenderregistered_viaregistration_init_timeis_churntotal_payment_channelschange_in_payment_methodspayment_plan_days_meanchange_in_planplan_list_price_meanactual_amount_paid_meanis_auto_renew_meanis_autorenew_change_flagtransaction_date_mintransaction_date_maxtotal_transactionsmembership_expire_date_maxis_cancel_meanis_cancel_change_flagdiscount_meanis_discount_meanis_discount_maxamt_per_day_meanmembership_duration_meanmore_than_30_sumnum_25num_50num_75num_985num_100num_unqtotal_secslogin_freqlast_loginregistration_duration
0252737T4TLe/jOIP0/Bg9kOKR3rd19XVZDIHlVzvS3DhpweIk=16.3242016-05-2219227.4444442132.444444132.4444440.00000002016-05-232017-01-0492017-02-030.00000000.00.004.41481541.22222261.0130380.5689680.1980420.0990211.2236182.0763687.078125142017-02-25257
1808333twUh3ugQEnQyGAyOxT4uPhpMht2EPW1vl6/k7J+RdI=826.0132015-04-11115141.0000002198.666667198.6666670.00000002015-04-112016-08-05152017-02-170.00000000.00.004.94119741.53333321.5837900.3593930.3379930.5376762.4520483.0384248.257812442017-01-21678
289564/1X44AKG3S5AoBAJeokIkqLvgRaz+h5ElPncLqWnWys=16.3272015-12-01014130.0000001149.000000149.0000001.00000012015-12-012017-02-28142017-03-310.00000000.00.004.96666730.28571471.3823770.3531170.2693480.4402053.8489933.8632089.406250272017-01-27486
31410701Ufg2Ep/dzom8G0hN+0UWbmxpRPFrGt4D4Vh2yd4pOA=16.3272016-09-1007130.0000001142.000000142.0000001.00000012016-09-102017-02-0372017-03-020.14285710.00.004.73333325.57142931.3788060.2070760.0577620.0577621.8657892.2574277.589844122017-01-19173
4182169ZozytlgZZhHBVCTHGug8CxREhiV9QpS+TJ5e+z06gVA=16.3272017-01-0602130.000000199.00000099.0000001.00000012017-01-062017-02-0522017-03-050.00000000.00.003.30000029.00000001.1634900.3465740.0000000.2746530.8664341.5992326.94140642017-01-2658
530877fsnsbXZaA/6BEIkHAEXt3yv0IKmO2BT7X6OVSryjLwE=1836.0142015-12-21015130.0000001149.000000149.0000001.00000012015-12-222017-02-22152017-03-230.00000000.00.004.96666731.400000130.7628620.2212650.2793720.2467183.5085223.5094029.062500362017-02-27458
6178732Pu+GJ7D/z/RdQdtkEC1FliAwgfqf4Lj2R+Yy8zpxIPI=1321.0142016-04-1809230.0000001149.000000149.0000000.77777812016-05-132017-02-0892017-03-070.00000000.00.004.96666729.44444401.9865560.8224060.3744670.0000000.9956932.6596427.16015682016-12-28323
71064404qn9IK9A4x98g5rvJzA/zs22i/cXKVnH2UxKIIohsmE=419.0072016-05-27010130.000000199.00000099.0000001.00000012016-05-272017-02-27102017-03-270.00000000.00.003.30000030.40000061.5775590.5889080.2942680.3572812.1651432.7955277.910156222017-02-28304
82347197Ch66cFMKjzoFmwEael1cZmL96xEXu1deuaISvbLeU4=16.3272016-07-1408130.000000199.00000099.0000001.00000012016-07-142017-02-1482017-03-140.00000000.00.003.30000030.37500052.0770860.3612960.1732870.0000001.6122022.3628547.07421982017-02-06243
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